{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9e7af7b-2c01-47f4-badb-87a94955a1e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cion.data import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eeafaf89-a04d-4732-9a12-e6359a2bb22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_time, list_values = raw_count(r\"D:\\Data\\2022\\202208\\20220815\\TimeScan-20220815162548.hdf5\")\n",
    "total_channels = 4\n",
    "repeat = len(list_values[0]) // total_channels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7ac5708-31e6-471d-b2ef-e8a28fa5d1ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_plot(r\"D:\\Data\\2022\\202208\\20220815\\TimeScan-20220815162548.hdf5\",threshold=[0,0,1,0], show_channels=[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b0a9b4f-bdea-4fec-9dfc-8c43d7f866be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "convert_matrix = np.eye(4)\n",
    "fit_paras = gaussian_fit(r\"D:\\Data\\2022\\202208\\20220815\\TimeScan-20220815162548.hdf5\", convert_matrix=convert_matrix, plot_figure=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f57fde05-da67-46ac-9198-e9e01a91695f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "convert_matrix = np.eye(4)\n",
    "half_cycle = rabi_fit(r\"D:\\Data\\2022\\202208\\20220815\\TimeScan-20220815162938.hdf5\", convert_matrix=convert_matrix, threshold=[0,0,1,0], plot_figure=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "597a8bce-8294-41d0-b48f-784b87ee9a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, 32095.8520830314, None]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a424c8c-fc9e-4f85-89f5-6dd49a7528ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 3., 3.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([np.float64(1),np.float64(1),np.float64(1)], dtype=np.float64)\n",
    "b = np.float64(3)\n",
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e022f12f-6df4-418d-90fb-50d76bd9b3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 3., 5.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 3., 5.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee291f3a-657f-4a16-b05c-f88c572e58aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a0262934-7c53-43df-b40b-bd5b438c3f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_time, list_values = raw_count(r\"D:\\Data\\2022\\202208\\20220815\\TimeScan-20220815162548.hdf5\")\n",
    "list_values = [np.mean(value,axis=0) for value in list_values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57b11f97-4a52-4fc1-b402-173b206272d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4,5,6,7,8]).reshape(2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa225f8b-198e-4939-8180-93bdc6629c93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d8764c-f495-4fdc-9e22-fc260051b306",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "433f5bdc-f5f2-4011-9a5e-f3791fb1570d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 100)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd2e4d10-c9bb-43aa-a9d8-d6be2f4cb9a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function subplot in module matplotlib.pyplot:\n",
      "\n",
      "subplot(*args, **kwargs)\n",
      "    Add an Axes to the current figure or retrieve an existing Axes.\n",
      "    \n",
      "    This is a wrapper of `.Figure.add_subplot` which provides additional\n",
      "    behavior when working with the implicit API (see the notes section).\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "       subplot(nrows, ncols, index, **kwargs)\n",
      "       subplot(pos, **kwargs)\n",
      "       subplot(**kwargs)\n",
      "       subplot(ax)\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    *args : int, (int, int, *index*), or `.SubplotSpec`, default: (1, 1, 1)\n",
      "        The position of the subplot described by one of\n",
      "    \n",
      "        - Three integers (*nrows*, *ncols*, *index*). The subplot will take the\n",
      "          *index* position on a grid with *nrows* rows and *ncols* columns.\n",
      "          *index* starts at 1 in the upper left corner and increases to the\n",
      "          right. *index* can also be a two-tuple specifying the (*first*,\n",
      "          *last*) indices (1-based, and including *last*) of the subplot, e.g.,\n",
      "          ``fig.add_subplot(3, 1, (1, 2))`` makes a subplot that spans the\n",
      "          upper 2/3 of the figure.\n",
      "        - A 3-digit integer. The digits are interpreted as if given separately\n",
      "          as three single-digit integers, i.e. ``fig.add_subplot(235)`` is the\n",
      "          same as ``fig.add_subplot(2, 3, 5)``. Note that this can only be used\n",
      "          if there are no more than 9 subplots.\n",
      "        - A `.SubplotSpec`.\n",
      "    \n",
      "    projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', 'polar', 'rectilinear', str}, optional\n",
      "        The projection type of the subplot (`~.axes.Axes`). *str* is the name\n",
      "        of a custom projection, see `~matplotlib.projections`. The default\n",
      "        None results in a 'rectilinear' projection.\n",
      "    \n",
      "    polar : bool, default: False\n",
      "        If True, equivalent to projection='polar'.\n",
      "    \n",
      "    sharex, sharey : `~.axes.Axes`, optional\n",
      "        Share the x or y `~matplotlib.axis` with sharex and/or sharey. The\n",
      "        axis will have the same limits, ticks, and scale as the axis of the\n",
      "        shared axes.\n",
      "    \n",
      "    label : str\n",
      "        A label for the returned axes.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    `.axes.SubplotBase`, or another subclass of `~.axes.Axes`\n",
      "    \n",
      "        The axes of the subplot. The returned axes base class depends on\n",
      "        the projection used. It is `~.axes.Axes` if rectilinear projection\n",
      "        is used and `.projections.polar.PolarAxes` if polar projection\n",
      "        is used. The returned axes is then a subplot subclass of the\n",
      "        base class.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs\n",
      "        This method also takes the keyword arguments for the returned axes\n",
      "        base class; except for the *figure* argument. The keyword arguments\n",
      "        for the rectilinear base class `~.axes.Axes` can be found in\n",
      "        the following table but there might also be other keyword\n",
      "        arguments if another projection is used.\n",
      "    \n",
      "        Properties:\n",
      "        adjustable: {'box', 'datalim'}\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "        alpha: scalar or None\n",
      "        anchor: (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}\n",
      "        animated: bool\n",
      "        aspect: {'auto', 'equal'} or float\n",
      "        autoscale_on: bool\n",
      "        autoscalex_on: bool\n",
      "        autoscaley_on: bool\n",
      "        axes_locator: Callable[[Axes, Renderer], Bbox]\n",
      "        axisbelow: bool or 'line'\n",
      "        box_aspect: float or None\n",
      "        clip_box: `.Bbox`\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        facecolor or fc: color\n",
      "        figure: `.Figure`\n",
      "        frame_on: bool\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        navigate: bool\n",
      "        navigate_mode: unknown\n",
      "        path_effects: `.AbstractPathEffect`\n",
      "        picker: None or bool or float or callable\n",
      "        position: [left, bottom, width, height] or `~matplotlib.transforms.Bbox`\n",
      "        prop_cycle: unknown\n",
      "        rasterization_zorder: float or None\n",
      "        rasterized: bool\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        title: str\n",
      "        transform: `.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xbound: unknown\n",
      "        xlabel: str\n",
      "        xlim: (bottom: float, top: float)\n",
      "        xmargin: float greater than -0.5\n",
      "        xscale: {\"linear\", \"log\", \"symlog\", \"logit\", ...} or `.ScaleBase`\n",
      "        xticklabels: unknown\n",
      "        xticks: unknown\n",
      "        ybound: unknown\n",
      "        ylabel: str\n",
      "        ylim: (bottom: float, top: float)\n",
      "        ymargin: float greater than -0.5\n",
      "        yscale: {\"linear\", \"log\", \"symlog\", \"logit\", ...} or `.ScaleBase`\n",
      "        yticklabels: unknown\n",
      "        yticks: unknown\n",
      "        zorder: float\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Creating a new Axes will delete any pre-existing Axes that\n",
      "    overlaps with it beyond sharing a boundary::\n",
      "    \n",
      "        import matplotlib.pyplot as plt\n",
      "        # plot a line, implicitly creating a subplot(111)\n",
      "        plt.plot([1, 2, 3])\n",
      "        # now create a subplot which represents the top plot of a grid\n",
      "        # with 2 rows and 1 column. Since this subplot will overlap the\n",
      "        # first, the plot (and its axes) previously created, will be removed\n",
      "        plt.subplot(211)\n",
      "    \n",
      "    If you do not want this behavior, use the `.Figure.add_subplot` method\n",
      "    or the `.pyplot.axes` function instead.\n",
      "    \n",
      "    If no *kwargs* are passed and there exists an Axes in the location\n",
      "    specified by *args* then that Axes will be returned rather than a new\n",
      "    Axes being created.\n",
      "    \n",
      "    If *kwargs* are passed and there exists an Axes in the location\n",
      "    specified by *args*, the projection type is the same, and the\n",
      "    *kwargs* match with the existing Axes, then the existing Axes is\n",
      "    returned.  Otherwise a new Axes is created with the specified\n",
      "    parameters.  We save a reference to the *kwargs* which we use\n",
      "    for this comparison.  If any of the values in *kwargs* are\n",
      "    mutable we will not detect the case where they are mutated.\n",
      "    In these cases we suggest using `.Figure.add_subplot` and the\n",
      "    explicit Axes API rather than the implicit pyplot API.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    .Figure.add_subplot\n",
      "    .pyplot.subplots\n",
      "    .pyplot.axes\n",
      "    .Figure.subplots\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    ::\n",
      "    \n",
      "        plt.subplot(221)\n",
      "    \n",
      "        # equivalent but more general\n",
      "        ax1 = plt.subplot(2, 2, 1)\n",
      "    \n",
      "        # add a subplot with no frame\n",
      "        ax2 = plt.subplot(222, frameon=False)\n",
      "    \n",
      "        # add a polar subplot\n",
      "        plt.subplot(223, projection='polar')\n",
      "    \n",
      "        # add a red subplot that shares the x-axis with ax1\n",
      "        plt.subplot(224, sharex=ax1, facecolor='red')\n",
      "    \n",
      "        # delete ax2 from the figure\n",
      "        plt.delaxes(ax2)\n",
      "    \n",
      "        # add ax2 to the figure again\n",
      "        plt.subplot(ax2)\n",
      "    \n",
      "        # make the first axes \"current\" again\n",
      "        plt.subplot(221)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.subplot)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "9f200e38-9a84-4977-ba2c-628d0ff7a731",
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   "source": []
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